BOL: Related items

S-plot2: Rapid Visual and Statistical Analysis of Genomic Sequences

Abhimanyu Singh — Tue, 02 Oct 2018 17:57:27 -0500

S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot

Nucl2Vec: Local alignment of DNA sequences using Distributed Vector Representation

Jit — Tue, 16 Mar 2021 05:45:44 -0500

We demonstrate a novel approach forlocal alignment of DNA reads with respect to reference genome.For this process we have used Skip-gram model for creatingencoding(Nucl2Vec) and k-nearest neighbor for the alignment.With our new approach we have reduced computation cost forlocal alignment , while achieving accuracy comparable to existingdefacto standard BWA-MEM tool.

https://prakharg24.github.io/papers/401851.full.pdf

Address of the bookmark: https://prakharg24.github.io/papers/401851.full.pdf

KAST: Perform Alignment-free k-tuple frequency comparisons from sequences

Jit — Thu, 20 Sep 2018 08:56:35 -0500

Perform Alignment-free k-tuple frequency comparisons from sequences. This can be in the form of two input files (e.g. a reference and a query) or a single file for pairwise comparisons to be made.

Address of the bookmark: https://github.com/martinjvickers/KAST

DECIPHER; a software toolset for deciphering and managing biological sequences efficiently using the R

BioStar — Sun, 09 Dec 2018 19:06:17 -0600

DECIPHER is a software toolset that can be used for deciphering and managing biological sequences efficiently using the R programming language. The R package is distributed as platform independent source code under the GPL version 3 license. Some functionality of the program is accessible online through web tools.

Address of the bookmark: http://www2.decipher.codes/

Miropeats: discovers regions of sequence similarity amongst any set of DNA sequences

Poonam Mahapatra — Mon, 26 Aug 2019 17:55:24 -0500

Miropeats discovers regions of sequence similarity amongst any set of DNA sequences and then presents this similarity information graphically. Sequence similarity searching is a very general tool that forms the basis of many different biological sequence analyses but it is limited by the verbosity of traditional alignment presentation styles. Miropeats enhances the utility of conventional DNA sequence comparisons when looking at long lengths of sequence similarity by summarizing extensive large scale sequence similarities on a single page of graphics. The latest version of Miropeats can be used as a general pairwise alignment program or in its traditional role sorting out a big mess of overlapping or similar regions.

Address of the bookmark: http://www.littlest.co.uk/software/bioinf/old_packages/miropeats/

NucDiff: In-depth characterization and annotation of differences between two sets of DNA sequences

Jit — Tue, 05 May 2020 10:35:48 -0500

NucDiff locates and categorizes differences between two closely related nucleotide sequences. It is able to deal with very fragmented genomes, structural rearrangements and various local differences. These features make NucDiff to be perfectly suitable to compare assemblies with each other or with available reference genomes.

NucDiff provides information about the types of differences and their locations. It is possible to upload the results into genome browser for visualization and further inspection. It was written in Python and uses the NUCmer package from MUMmer[1] for sequence comparison.

Address of the bookmark: https://github.com/uio-cels/NucDiff

Steps to find palindrome in genomes !

BioStar — Thu, 09 Mar 2023 02:56:54 -0600

Palindromes are sequences of nucleotides that read the same backward as forward. They can be present in genomes and have various biological functions. Here are some methods for discovering palindromes in genomes:

Direct sequence search: One of the simplest ways to discover palindromes is to search the genome sequence directly for palindromic sequences using pattern matching tools, such as regular expressions or string algorithms. This approach can be useful for discovering simple palindromes, but may miss more complex palindromic structures.
Dot plot analysis: Dot plot analysis is a graphical method that can be used to identify palindromic regions in a genome. It involves plotting the genome sequence against itself and examining the diagonal patterns that emerge. Palindromic regions will appear as symmetrical patterns along the diagonal.
Restriction enzyme analysis: Some restriction enzymes, such as EcoRI and HindIII, recognize palindromic sequences and cleave DNA at these sites. By digesting the genome with these enzymes and examining the resulting fragments, palindromic regions can be identified.
Next-generation sequencing: High-throughput sequencing technologies, such as PacBio and Oxford Nanopore, can generate long reads that can span entire palindromic regions. By mapping these reads to the genome, palindromic regions can be identified and characterized.
Comparative genomics: Comparing the genomes of related species can also reveal palindromic regions that are conserved across evolutionarily divergent lineages. This approach can help identify functional palindromes that are under selective pressure.

Overall, the discovery of palindromic sequences in genomes can be accomplished using a variety of methods, each with their own advantages and limitations. A combination of these methods can provide a comprehensive understanding of the palindromic landscape of a genome.

Jaeger : an accurate and fast deep-learning tool to detect bacteriophage sequences

Neel — Thu, 14 Aug 2025 04:02:05 -0500

Jaeger is a tool that utilizes homology-free machine learning to identify phage genome sequences that are hidden within metagenomes. It is capable of detecting both phages and prophages within metagenomic assemblies.

The performance of the Jaeger workflow can be significantly increased by utilizing GPUs. To enable GPU support, the CUDA Toolkit and cuDNN library must be accessible to conda.

# setup bioconda
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
conda config --set channel_priority strict

# create conda environment and install jaeger
mamba create -n jaeger -c nvidia -c conda-forge cuda-nvcc "python>=3.9,<3.12" pip jaeger-bio


# activate environment
conda activate jaeger

Address of the bookmark: https://github.com/MGXlab/Jaeger

Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads

BioStar — Tue, 04 Feb 2020 23:23:16 -0600

Rcorrector has an accuracy higher than or comparable to existing methods, including the only other method (SEECER) designed for RNA-seq reads, and is more time and memory efficient. With a 5 GB memory footprint for 100 million reads, it can be run on virtually any desktop or server. The software is available free of charge under the GNU General Public License from https://github.com/mourisl/Rcorrector/.

Usage: perl run_rcorrector.pl [OPTIONS]
OPTIONS:
	Required
	-s seq_files: comma separated files for single-end data sets
	-1 seq_files_left: comma separated files for the first mate in the paried-end data sets
	-2 seq_files_right: comma separated files for the second mate in the paired-end data sets
	-i seq_files_interleaved: comma sperated files for interleaved paired-end data sets
	Optional
	-k INT: kmer_length (<=32, default: 23)
	-od STRING: output_file_directory (default: ./)
	-t INT: number of threads to use (default: 1)
	-trim : allow trimming (default: false)
	-maxcorK INT: the maximum number of correction within k-bp window (default: 4)
	-wk FLOAT: the proportion of kmers that are used to estimate weak kmer count threshold, lower for more divergent genome (default: 0.95)
	-ek INT: expected number of kmers; does not affect the correctness of program but affects the memory usage (default: 100000000)
	-stdout: output the corrected reads to stdout (default: not used)
	-verbose: output some correction information to stdout (default: not used)
	-stage INT: start from which stage (default: 0)
		0-start from begining(storing kmers in bloom filter) ;
		1-start from count kmers showed up in bloom filter;
		2-start from dumping kmer counts into a jf_dump file;
		3-start from error correction.

Address of the bookmark: https://github.com/mourisl/Rcorrector/

PuffAligner: a fast, efficient and accurate aligner based on the Pufferfish index

Rahul Nayak — Thu, 21 Apr 2022 05:41:39 -0500

PuffAligner, a fast, accurate and versatile aligner built on top of the Pufferfish index. PuffAligner is able to produce highly sensitive alignments, similar to those of Bowtie2, but much more quickly. While exhibiting similar speed to the ultrafast STAR aligner, PuffAligner requires considerably less memory to construct its index and align reads. PuffAligner strikes a desirable balance with respect to the time, space and accuracy tradeoffs made by different alignment tools and provides a promising foundation on which to test new alignment ideas over large collections of sequences.

Address of the bookmark: https://github.com/COMBINE-lab/pufferfish/tree/cigar-strings